<p>This thesis presents a method for predicting radon (222Rn) levels in groundwater on a general scale, within an area of approximately 185 x 145 km2. The method applies to Swedish conditions, where 222Rn is the main contributor to natural radioactivity. Prediction of radon potential in groundwater is complex because there are many different factors affecting radon content, including geochemical and flow processes. The proposed method is based on univariate and multivariate statistical analyses and investigated the influence of different factors such as bedrock, soils, uranium distribution, altitude, distance to fractures and land use. A statistical variable based method (the RV method) was used to estimate risk values related to different radon concentrations. The method was calibrated and tested on more than 4400 drilled wells in Stockholm County. The weighted index (risk value) estimated by the RV method provided a fair prediction of radon potential in groundwater on a general scale. The RV method was successful in estimating the median radon concentration within 12 subregions (at a local scale, each of area 25 x 25 km2), based on weighted index values obtained from half of all wells tested. A high correlation between risk values and median radon concentrations was demonstrated. The factors bedrock, altitude, distance to fracture zone and distribution of uranium in bedrock were found to be significant in the prediction approach on a general scale. Visual data mining, which comprised analysis of 3D images, was a useful tool for data exploration but could not be used as an independent method for drawing conclusions regarding radon in groundwater. Results of a field study based on 38 drilled wells on the island of Ljusterö in the Stockholm archipelago showed that 222Rn concentrations in groundwater were weakly correlated to the parent elements (226Ra and 238U) in solution.</p>

This thesis presents a method for predicting radon (222Rn) levels in groundwater on a general scale, within an area of approximately 185 x 145 km2. The method applies to Swedish conditions, where 222Rn is the main contributor to natural radioactivity. Prediction of radon potential in groundwater is complex because there are many different factors affecting radon content, including geochemical and flow processes. The proposed method is based on univariate and multivariate statistical analyses and investigated the influence of different factors such as bedrock, soils, uranium distribution, altitude, distance to fractures and land use. A statistical variable based method (the RV method) was used to estimate risk values related to different radon concentrations. The method was calibrated and tested on more than 4400 drilled wells in Stockholm County. The weighted index (risk value) estimated by the RV method provided a fair prediction of radon potential in groundwater on a general scale. The RV method was successful in estimating the median radon concentration within 12 subregions (at a local scale, each of area 25 x 25 km2), based on weighted index values obtained from half of all wells tested. A high correlation between risk values and median radon concentrations was demonstrated. The factors bedrock, altitude, distance to fracture zone and distribution of uranium in bedrock were found to be significant in the prediction approach on a general scale. Visual data mining, which comprised analysis of 3D images, was a useful tool for data exploration but could not be used as an independent method for drawing conclusions regarding radon in groundwater. Results of a field study based on 38 drilled wells on the island of Ljusterö in the Stockholm archipelago showed that 222Rn concentrations in groundwater were weakly correlated to the parent elements (226Ra and 238U) in solution. / QC 20101221